Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the num...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/15/2495 |
| _version_ | 1797560384322797568 |
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| author | Ava Vali Sara Comai Matteo Matteucci |
| author_facet | Ava Vali Sara Comai Matteo Matteucci |
| author_sort | Ava Vali |
| collection | DOAJ |
| description | Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field. |
| first_indexed | 2024-03-10T17:59:47Z |
| format | Article |
| id | doaj.art-286a8cd43eff490e9349e43439c6fc16 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T17:59:47Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-286a8cd43eff490e9349e43439c6fc162023-11-20T08:58:33ZengMDPI AGRemote Sensing2072-42922020-08-011215249510.3390/rs12152495Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A ReviewAva Vali0Sara Comai1Matteo Matteucci2Department of Electronics, Information and Bioengineering, Polytechnic of Milan University, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Polytechnic of Milan University, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Polytechnic of Milan University, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyLately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.https://www.mdpi.com/2072-4292/12/15/2495remote sensing datahyperspectral datamultispectral dataLULC classificationmachine learningdeep Learning |
| spellingShingle | Ava Vali Sara Comai Matteo Matteucci Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review Remote Sensing remote sensing data hyperspectral data multispectral data LULC classification machine learning deep Learning |
| title | Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review |
| title_full | Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review |
| title_fullStr | Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review |
| title_full_unstemmed | Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review |
| title_short | Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review |
| title_sort | deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data a review |
| topic | remote sensing data hyperspectral data multispectral data LULC classification machine learning deep Learning |
| url | https://www.mdpi.com/2072-4292/12/15/2495 |
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